Abstract
While high resolution satellite remote sensing has been hailed as a very useful source of data for biodiversity assessment and monitoring, applications have been more developed in temperate areas. The biodiverse tropics offer a challenge of an altogether different magnitude for hyperspatial and hyperspectral remote sensing. This paper examines issues related to hyperspatial and hyperspectral remotely sensed imagery, which constitutes one of the most potentially powerful yet underutilized sources of for tropical research on biodiversity. Hyperspatial data with their increased pixel resolution are possibly best suited at facilitating the accurate location of features such as tree canopies, but less suited to the identification of aspects such as species identity, particularly when spatial resolution becomes too fine and pixels are smaller than the size of the object (e.g., tree canopy) being identified. Hyperspectral data on the other hand, with their high spectral resolution, can be used to record information pertaining to a range of critical plant properties related to species identity, and can be very effective used for discriminating tree species in tropical forests, despite the greater complexity of such environments. There remains a glaring gap in the easy availability of hyperspectral and hyperspatial satellite data in the tropics due to reasons of cost, data coverage, and security restrictions. Stimulating discussion on the applications of this powerful, but underutilized tool by ecologists, is the first step in promoting a more extensive use of such data for ecological studies in tropical biodiversity rich areas.
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References
Carlson KM, Asner GP, Hughes RF, Ostertag R, Martin RE (2007) Hyperspectral remote sensing of canopy biodiversity in Hawaiian lowland rainforests, NY, Print. Ecosystems 10:536–549. doi:10.1007/s10021-007-9041-z
Clark M, Roberts DA, Clark DB (2005) Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sens Environ 96:375–398. doi:10.1016/j.rse.2005.03.009
Cochrane MA (2000) Using vegetation reflectance variability for species level classification of hyperspectral data. Int J Remote Sens 21:2075–2087. doi:10.1080/01431160050021303
Curran PJ (1989) Remote sensing of foliar chemistry. Remote Sens Environ 30:271–278. doi:10.1016/0034-4257(89)90069-2
Dormann CF (2007) Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Glob Ecol Biogeogr 16:129–138. doi:10.1111/j.1466-8238.2006.00279.x
Duro DC, Coops NC, Wulder MA, Han T (2007) Development of a large area monitoring system driven by remote sensing. Prog Phys Geogr 31:235–260. doi:10.1177/0309133307079054
Fairbanks DHK, McGwire KC (2004) Patterns of floristic richness in vegetation communities of California: regional scale analysis with multi-temporal NDVI. Glob Ecol Biogeogr 13:221–235. doi:10.1111/j.1466-822X.2004.00092.x
Foody GM, Cutler MEJ (2003) Tree biodiversity in protected and logged Bornean tropical rain forests and its measurement by satellite remote sensing. J Biogeogr 30:1053–1066
Fraser CS, Dial G, Grodecki J (2006) Sensor orientation via RPCs. ISPRS J Photogramm Remote Sens 60:182–194. doi:10.1016/j.isprsjprs.2005.11.001
Fuller DO (2005) Remote detection of invasive Melaleuca trees (Melaleuca quinquenervia) in South Florida using multispectral IKONOS imagery. Int J Remote Sens 26:1057–1063
Geist HJ, Lambin EF (2002) Proximate causes and underlying driving forces of tropical deforestation. Bioscience 52:143–150. doi:10.1641/0006-3568(2002)052[0143:PCAUDF]2.0.CO;2
Gillespie TW (2006) Predicting woody-plant species richness in tropical dry forests: a case study from South Florida, USA. Ecol Appl 15:27–37. doi:10.1890/03-5304
Gillespie TW, Foody GM, Rocchini D, Giorgi AP, Saatchi S (2008) Measuring and modelling biodiversity from space. Prog Phys Geogr 32:203–221. doi:10.1177/0309133308093606
Goetz S (2007) Crisis in Earth observation. Science 315:1767. doi:10.1126/science.1142466
Hernández-Stefanoni JL, Dupny JM (2007) Mapping species density of trees, shrubs and vines in a tropical forest, using field measurements, satellite multispectral imagery and spatial interpolation. Biodivers Conserv 16:3817–3833. doi:10.1007/s10531-007-9182-6
Im J, Rhee J, Jensen JR, Hodgson ME (2007) An automated binary change detection model using a calibration approach. Remote Sens Environ 106:89–105. doi:10.1016/j.rse.2006.07.019
Innes JL, Koch B (1998) Forest biodiversity and its assessment by remote sensing. Glob Ecol Biogeogr Lett 7:397–419. doi:10.2307/2997712
Jakubauskas ME, Price KP (1997) Empirical relationships between structural and spectral factors of Yellowstone Lodgepole Pine forests. Photogramm Eng Remote Sens 63:1375–1381
Johansen K, Coops NC, Gergel SE, Stange Y (2007) Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification. Remote Sens Environ 110:29–44. doi:10.1016/j.rse.2007.02.014
Kalacksa M, Sanchez-Azofeifa GA, Rivard B, Caelli T, White HP, Calvo-Alvarado JC (2007) Ecological fingerprinting of ecosystem sucession: estimating secondary tropical dry forest structure and spectroscopy. Remote Sens Environ 108:82–96. doi:10.1016/j.rse.2006.11.007
Kark S, Levin N, Phinn S (2008) Global environmental priorities: making sense of remote sensing: reply to TREE Letter: satellites miss environmental priorities by Loarie et al. (2007). Trends Ecol Evol 23:181–182. doi:10.1016/j.tree.2008.01.001
Kayitakire F, Hamel C, Defourny P (2006) Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery. Remote Sens Environ 102:390–401. doi:10.1016/j.rse.2006.02.022
Kerr JT, Ostrovsky M (2003) From space to species: ecological applications for remote sensing. Trends Ecol Evol 18:299–305. doi:10.1016/S0169-5347(03)00071-5
Laba M, Downs R, Smith S, Welsh S, Neider C, White S et al (2008) Mapping invasive wetland plants in the Hudson River National Estuarine Research Reserve using quickbird satellite imagery. Remote Sens Environ 112:286–300. doi:10.1016/j.rse.2007.05.003
Lassau SA, Cassis G, Flemons PKJ, Wilkie L, Hochuli DF (2005) Using high-resolution multi-spectral imagery to estimate habitat complexity in open-canopy forests: can we predict ant community patterns? Ecography 28:495–504. doi:10.1111/j.0906-7590.2005.04116.x
Legendre P (1993) Spatial autocorrelation: trouble or new paradigm? Ecology 74:1659–1673. doi:10.2307/1939924
Levin N, Shmida A, Levanoni O, Tamari H, Kark S (2007) Predicting mountain plant richness and rarity from space using satellite-derived vegetation indices. Divers Distrib 13:692–703
Ling Y, Ehlers M, Usery EL, Madden M (2007) FFT-enhanced IHS transform method for fusing high-resolution satellite images. ISPRS J Photogramm Remote Sens 61:381–392. doi:10.1016/j.isprsjprs.2006.11.002
Loarie SR, Joppa LN, Pimm SL (2007) Satellites miss environmental priorities. Trends Ecol Evol 22:630–632. doi:10.1016/j.tree.2007.08.018
Martin ME, Aber JD (1997) High spectral resolution remote sensing of forest canopy lignin, nitrogen, and ecosystem processes. Ecol Appl 7:431–443. doi:10.1890/1051-0761(1997)007[0431:HSRRSO]2.0.CO;2
Mehner H, Cutler M, Fairbairn D, Thompson G (2004) Remote sensing of upland vegetation: the potential of high spatial resolution satellite sensors. Glob Ecol Biogeogr 13:359–369. doi:10.1111/j.1466-822X.2004.00096.x
Nagendra H (2001) Using remote sensing to assess biodiversity. Int J Remote Sens 22:2377–2400. doi:10.1080/01431160117096
Nagendra H, Gadgil M (1999) Satellite imagery as a tool for monitoring species diversity: an assessment. J Appl Ecol 36:388–397
Nagendra H, Pareeth S, Sharma B, Schweik CM, Adhikari KA (2008) Forest fragmentation and regrowth in an institutional mosaic of community, government and private ownership in Nepal. Landsc Ecol 23:41–54. doi:10.1007/s10980-007-9162-y
Nichol J, Wong MS (2007) Remote sensing of urban vegetation life form by spectral mixture analysis of high-resolution IKONOS satellite images. Int J Remote Sens 28:985–1000. doi:10.1080/01431160600784176
Olthof I, Fraser RH (2007) Mapping northern land cover fractions using Landsat ETM+. Remote Sens Environ 107:496–509. doi:10.1016/j.rse.2006.10.009
Ostrom E, Nagendra H (2006) Insights on linking forests, trees, and people from the air, on the ground and in the air. Proc Natl Acad Sci USA 103:19224–19231. doi:10.1073/pnas.0607962103
Palmer MW, Earls P, Hoagland BW, White PS, Wohlgemuth T (2002) Quantitative tools for perfecting species lists. Environmetrics 13:121–137. doi:10.1002/env.516
Read JM, Clark DB, Venticinque EM, Moreiras MP (2003) Application of merged 1-m and 4-m resolution satellite data to research and management in tropical forests. J Appl Ecol 40:592–600. doi:10.1046/j.1365-2664.2003.00814.x
Ricotta C, Avena GC, Volpe F (1999) The influence of principal component analysis on the spatial structure of a multispectral dataset. Int J Remote Sens 20:3367–3376. doi:10.1080/014311699213712
Rocchini D (2007) Effects of spatial and spectral resolution in estimating ecosystem α-diversity by satellite imagery. Remote Sens Environ 111:423–434. doi:10.1016/j.rse.2007.03.018
Rocchini D, Vannini A (2008) What is up? Testing spectral heterogeneity vs. NDVI relationship by quantile regression. Int J Remote Sens (in press)
Rocchini D, Chiarucci A, Loiselle SA (2004) Testing the spectral variation hypothesis by using satellite multispectral images. Acta Oecol 26:117–120. doi:10.1016/j.actao.2004.03.008
Rocchini D, Andreini Butini S, Chiarucci A (2005) Maximizing plant species inventory efficiency by means of remotely sensed spectral distances. Glob Ecol Biogeogr 14:431–437. doi:10.1111/j.1466-822x.2005.00169.x
Sanchez-Azofeifa GA, Castro KL, Rivard B, Kalascka MR, Harriss RC (2003) Remote sensing research priorities in tropical dry forest environments. Biotropica 35:134–142
Small C (2004) The Landsat ETM+ spectral mixing space. Remote Sens Environ 93:1–17. doi:10.1016/j.rse.2004.06.007
Sohn G, Dowman I (2007) Data fusion of high-resolution satellite imagery and LiDAR data for automatic building extraction. ISPRS J Photogramm Remote Sens 62:43–63. doi:10.1016/j.isprsjprs.2007.01.001
Song C, Woodcock CE (2002) The spatial manifestation of forest succession in optical imagery: the potential of multiresolution imagery. Remote Sens Environ 82:271–284. doi:10.1016/S0034-4257(02)00045-7
Thenkabail PS, Enclona EA, Ashton MA, Legg C, Dieu MJD (2004) Hyperion, IKONOS, ALI and ETM+ sensors in the study of African rainforests. Remote Sens Environ 90:23–43. doi:10.1016/j.rse.2003.11.018
Townsend AR, Asner GP, Cleveland CC (2008) The biogeochemical heterogeneity of tropical forests. Trends Ecol Environ 43(8):424–431. doi:10.1016/j.tree.2008.04.009
Turner W, Spector S, Gardiner N, Fladeland M, Sterling E, Steininger M (2003) Remote sensing for biodiversity science and conservation. Trends Ecol Evol 18:306–314. doi:10.1016/S0169-5347(03)00070-3
Verlinden A, Masogo R (1997) Satellite remote sensing of habitat suitability for ungulates and ostrich in the Kalahari of Botswana. J Arid Environ 35:563–574. doi:10.1006/jare.1996.0174
Wagner HH (2003) Spatial covariance in plant communities: integrating ordination, geostatistics, and variance testing. Ecology 84:1045–1057. doi:10.1890/0012-9658(2003)084[1045:SCIPCI]2.0.CO;2
Wu J, Wang D, Bauer ME (2005) Image-based atmospheric correction of quickbird imagery of Minnesota cropland. Remote Sens Environ 99:315–325. doi:10.1016/j.rse.2005.09.006
Wulder MA, Hall RJ, Coops NC, Franklin SE (2004) High spatial resolution remotely sensed data for ecosystem characterization. Bioscience 54:511–521. doi:10.1641/0006-3568(2004)054[0511:HSRRSD]2.0.CO;2
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We would like to thank the editors and three anonymous referees for their useful comments, and the Society in Science: Branco Weiss fellowship for financial assistance to HN.
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Nagendra, H., Rocchini, D. High resolution satellite imagery for tropical biodiversity studies: the devil is in the detail. Biodivers Conserv 17, 3431–3442 (2008). https://doi.org/10.1007/s10531-008-9479-0
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DOI: https://doi.org/10.1007/s10531-008-9479-0